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Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the meta-heuristic algorithm for solving NP-hard problems in both continuous and discrete optimization fields, such as nonlinear and multi-level programming problems through extensive simulations of volcano eruption process. In particular, a new optimization solution named Volcano Eruption Algorithm (VEA) proposed in this paper, which is inspired from
the nature of volcano eruption. The feasibility and efficiency of the algorithm are evaluated using numerical results obtained through several test problems reported in the state-of-theart literature. Based on the solutions and number of required iterations, we observed that the proposed meta-heuristic algorithm performs remarkably well to solve NP-hard problem.
Furthermore, the proposed algorithm is applied to solve some large-size benchmarking LP and Internet of Vehicles (IoV) problems efficiently.

Acidic amino acids, aspartic acid (Asp) and glutamic acid (Glu) can enhance the solubility of many poorly soluble drugs including ciprofloxacin (Cip). One of the mechanisms of resistance within a biofilm is retardation of drug diffusion due to poor penetration across the matrix. To overcome this challenge, this work set to investigate novel counter ion approach with acidic amino acids, which we hypothesised will disrupt the biofilm matrix as well as simultaneously improve drug effectiveness. The anti-biofilm activity of D-Asp and D-Glu was studied on Staphylococcus aureus biofilms. Synergistic effect of combining D-amino acids with Cip was also investigated as a strategy to overcome anti-microbial resistance in these biofilms. Interestingly at equimolar combinations, D-Asp and D-Glu were able to significantly disperse (at 20 mM and 40 mM) established biofilms and inhibit (at 10 mM, 20 mM and 40 mM) new biofilm formation in the absence of an antibiotic. Moreover, our study confirmed L-amino acids also exhibit anti-biofilm activity. The synergistic effect of acidic amino acids with Cip was observed at lower concentration ranges (<40 mM amino acids and <90.54 µM, respectively), which resulted in 96.89% (inhibition) and 97.60% (dispersal) reduction in CFU with exposure to 40 mM amino acids. Confocal imaging indicated that the amino acids disrupt the honeycomb-like extracellular DNA (eDNA) meshwork whilst also preventing its formation.

Metaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of
a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first “MWE-aware” metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets.

Purpose: In December 2019, news broke out from the World Health Organisation (WHO),
with the first outbreak of Covid-19 in Wuhan China. On March 11 2020, the WHO declared
Covid-19 to officially be a pandemic. The UK was put under lockdown on the 23rd of March
2020 by the Prime Minister Boris Johnson, the lockdown introduced strict measures put into
place including the restriction for unnecessary working from offices, and only leaving houses
if it is deemed essential. Therefore, this paper focuses on the changes that the infrastructure
sector has had to undertake in order to adhere to the Covid-19 lockdown rules while being
productive.
Design/methodology/approach: Given the new and unexplored nature of the research
problem, a qualitative research methodology was adopted. In total, five semi-structured
interviews from three infrastructure sector organisations were conducted to collect data,
which was then analysed using thematic analysis for inference and conclusion.
Findings: The results indicate that the lockdown is proving to be difficult to manage projects
as staff members are working from home. This leads to delays on a project activities as many
staff cannot physically go on site and conduct works. The managers are finding difficult to
manage their teams. However, technological tools such as video chat and meetings via online
platforms have proven to be most effective in communications with project teams. The
Building Information Modelling (BIM)/Design has been useful as the 3D design models
helps to visualise the project within team meetings in order to comply with the Covid-19
rules and follow social distancing guidelines while still carrying on works. However,
induction to any new starter is proving to be difficult to manage with the pandemic and
lockdown as it involves a drugs and alcohol test prior to commencing work of that project.
Originality/value: This paper provides a rich insight into the understanding and awareness of
the impact of Covid-19 and the changes that the infrastructure sector has had to undertake in
order to adhere to the lockdown rules while being productive. This study contributes towards
informing policymakers on some lessons learned from the management of the Covid-19 from
an infrastructure sector perspective. Furthermore, twelve key implications are drawn for
decision makers within the infrastructure sector business to rethink and act to deal with the
pandemic crisis.

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